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Gaussian Processes

Gaussian Processes is a powerful framework for several machine learning tasks such as regression, classification and inference. Given a finite set of input output training data that is generated out of a fixed (but possibly unknown) function, the framework models the unknown function as a stochastic process such that the training outputs are a finite number of jointly Gaussian random variables, whose properties can then be used to infer the statistics (the mean and variance) of the function at test values of input.

Source: Sequential Randomized Matrix Factorization for Gaussian Processes: Efficient Predictions and Hyper-parameter Optimization

Papers

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Showing 1701–1710 of 1963 papers

TitleStatusHype
Adversarial Examples, Uncertainty, and Transfer Testing Robustness in Gaussian Process Hybrid Deep Networks—0
Location Dependent Dirichlet Processes—0
Variational Bayesian Multiple Instance Learning With Gaussian ProcessesCode0
Correlational Gaussian Processes for Cross-Domain Visual Recognition—0
Statistical abstraction for multi-scale spatio-temporal systems—0
Scalable Multi-Class Gaussian Process Classification using Expectation Propagation—0
Data-Efficient Reinforcement Learning with Probabilistic Model Predictive ControlCode0
Learning to Detect Sepsis with a Multitask Gaussian Process RNN ClassifierCode0
Dealing with Integer-valued Variables in Bayesian Optimization with Gaussian ProcessesCode0
Scaling up the Automatic Statistician: Scalable Structure Discovery using Gaussian Processes—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ICKy, periodicRoot mean square error (RMSE)0.03—Unverified